Consumption Of Electric Vehicle Research Articles

Influence Factors and Correction coefficient for Energy Consumption of Electric Vehicles

With the application of electric vehicles, the problem of energy consumption has been widely concerned. Fixed operating conditions energy consumption in the laboratory environment was used for vehicle identification, which made big difference from the actual situation. The paper was based on the actual operation data of the vehicles. The influence factors of energy consumption have been put forward through theoretical analysis. The correction factor was calculated by big data analysis and statistical fitting. The actual energy consumption of electric vehicles can be more accurately estimated through the research results.

Integration of a Pilot PV Parking Lot and an Electric Vehicle in a University Campus Located in Curitiba: a Study Case

HIGHLIGHTS Increasing electric vehicles against utility grid energy supplying capacity. Solar Carport system as a solution to provide energy for EV recharging needs. Performance analysis of a real solar carport energy production. Comparison between energy production and electric vehicle consumption for a day use.

Pricing strategy and coordination of automobile manufacturers based on government intervention and carbon emission reduction

The game model of the duopoly automobile manufacturers established in this paper takes the carbon emission reduction policy constraint as the research background, and discusses how the electric vehicle and the fuel vehicle compete in the performance of the product in the delay pricing decision under the strategic consumer, which is seldom considered in other related studies. The government gives electric vehicle consumers preferential policies on consumption subsidies and exemption from consumption tax. Additionally, he levies consumption tax and sales carbon tax from fuel vehicle consumers and manufacturers, respectively. The government also supports the development of electric vehicles by establishing charging piles. This paper cast light on how different market structures and different adjusting speed of price work on the vehicle manufacturers' operation and the system's stability. At first, we consider a single period game to derive the optimal solutions, finding that government intervention policies can maximize the social welfare. Additionally, the impacts of tax and subsidy on social welfare are different when fuel vehicle manufacturer is leader. Then, we consider a repeated game where players make decision based on bounded rationality. We compare the optimal solutions and give a numerical simulation for the dynamic process. For fuel vehicle manufacturer, too much energy consumption triggers high emission tax and also lowers consumers' surplus. We also find that fuel vehicle manufacturer acting as the leader enables the system to be more stable than the scenario in the current vehicle industry condition, where the electric vehicle manufacturer acts as the leader.

Exploring the factors influencing electric vehicle adoption: an empirical investigation in the emerging economy context of India

Purpose The Indian automotive industry was witnessing a transition from conventional vehicles to greener battery-operated electric vehicles (EVs). However, the acceptance of these EVs was still muted and brought significant challenges for the industry. Literature regarding the adoption of EVs was scarce in the Indian context. It was thus imperative to explore and comprehend the distinct perceptions of industry managers and consumers regarding the adoption of EVs in India. The purpose of this study is to comprehensively analyze the entire Indian EVs ecosystem to address this research gap. Design/methodology/approach The authors carried out an empirical investigation starting with a structured literature review to identify the researchable gaps. Subsequently, the authors conducted semi-structured open-ended interviews with 38 experts including automotive industry experts and EV consumers. The authors further performed a thematic content analysis of the expert interview responses to document critical insights regarding the adoption of EVs. Findings The authors identified 11 key factors influencing the adoption of EVs in this study. The vital considerations regarding the availability of charging technologies, its associated selection dilemma, emerging business models and public policy support were presented and discussed. Market penetration of EVs was found to be influenced mostly by the choice of charging technology. Further, the switching intention of consumers was deliberated upon to highlight the specific technological and psychological preferences of consumers. The accessibility of charging stations emerged as the most influential factor. The research findings indicated that harmony among stakeholders was missing in the Indian EVs ecosystem. Instead, there were discrete efforts by organizations. The EVs ecosystem required collaboration for improved adoption of the EVs. Further, the necessity to rectify the chaotic charging infrastructure in the country was highlighted as a major pain point for customers to adopt EV. Research limitations/implications This study theoretically contributed to push–pull–mooring (PPM) framework for understanding the adoption of EVs in India. This enabled the authors to extensively analyze consumers’ psychological and technological considerations regarding their switching intention toward EVs. Practical implications The findings of this study would help managers in decision-making toward the establishment of charging infrastructure involving multiple considerations such as the accessibility of charging, multi-dimensional competence at charging stations and servicing capabilities. Managers could also use the insights from this study to secure supportive recommendations for improving the overall EV infrastructure. The results of this study would benefit policymakers to set strategic directions through an integrated view of the entire EVs ecosystem involving management of bus and taxi fleets, two-wheelers and three-wheelers and such others. Originality/value Generally, in extant research, either firm managers’ or customers’ perspectives are considered separately. This study deliberated upon the PPM framework and switching intention accommodating both the industry and consumers’ perspectives. To the best of the authors’ knowledge, this was, thus, one of the first research articles which integrated insights from both the industry and consumers. This established the PPM framework for understanding the adoption of EVs. Further, it helped in comprehending the specific technological and psychological preferences of consumers regarding switching intention toward EVs.

Predictive model for energy consumption of battery electric vehicle with consideration of self-uncertainty route factors

Greenhouse gas emissions caused by urban road mobility have reached unsustainable levels, being partly responsible for both, the current global warming alarming situation and the pollutant emissions risk to the health of citizens. Transport electrification represents an encouraging solution to this problem. The development and improvement of electric vehicle technology combined with transportation management strategies have become relevant research topics, not only in the area of mobility of people, but also to large companies whose supply chain may involve hundreds of commercial vehicles. The costs from running these vehicles represent a significant impact on their finance and controlling fuel usage is often a key contributor to keep running costs under control across their fleet and supply chain. In this context, traffic regulation elements evaluation, considering its uncertainty, could represent a significant influence on the vehicle consumption and, consequently, on its indirect greenhouse gas emissions. For this reason, traffic regulation system analysis may represent a step forward for developing solutions that collaborate with both, pollution and global warming, abatement. The present study proposes a predictive methodology for determining battery electric vehicle consumption variations when modifying traffic regulation elements. It uses stochastic speed profiles for neutralizing human intervention in consumption and multiple linear regressions to predict the energy consumed by the electric vehicle as a function of a set of factors that represent the traffic regulations. The research does not aim to provide particular energy consumption data, but to expose the variations in consumption and emissions caused by urban planning modifications. Model accuracy and achieved conclusions are illustrated through the development of a case study. The methodology could be a help for urban planners, fleet managers, logistics and supply chain decision makers environmentally concerned.

Research on the Development and Application of Charging Piles Based on the Development of New Energy Vehicles

In this paper, based on the cloud computing platform, the reasonable design of the electric vehicle charging pile can not only effectively solve various problems in the process of electric vehicle charging, but also enable the electric vehicle users to participate in the power management. It makes the charging and power consumption of electric vehicles more scientific and reasonable, realizes the communication and interaction between users and power grid, meets the charging requirements of electric vehicles, improves the overall service quality, and contributes to the comprehensive promotion of the technology.

A Novel Methodology for Evaluating the Impact of Energy Efficiency Measures on the Cabin Thermal Comfort of Electric Vehicles

Climate control systems have a largely negative effect on the energy consumption of electric vehicles and consequently on their real driving range. Improving the efficiency of climate control systems requires advanced simulation tools for an accurate evaluation of both the energy savings and thermal comfort of innovative heating and cooling solutions. In this study, the advancements beyond the state of the art consists primarily of the methodology tackling the reduction of computational costs of intensive computational fluid dynamics (CFD) simulations and/or time-consuming experimental investigations and the simultaneous assessment of vehicle cabin thermal comfort and energy flows. The approach was validated against climatized chassis dyno measurements from the EU Horizon 2020 research project QUIET. Indeed, all the considered locations within the cabin were properly validated, both in steady state and transient conditions with the largest deviations at steady state below 3 °C. Additionally, the validation results show a perfect agreement for the average cabin predicted mean vote (PMV) value and a largest deviation in terms of the PMV for the other locations below 0.3. Furthermore, the applicability of the methodology is proved with the help of its application on a parametric study for which various cabin temperature setpoints and heating, ventilation and air conditioning (HVAC) modes were simulated in winter operation.

Forecasted datasets of electric vehicle consumption on the electricity grid of Spain

The information included in this study were calculated on the basis of data provided by the Spanish electricity grid, for thirteen years between 2007 and 2019. This data includes: the average consumption demand on the Spanish electricity grid at national level, and its availability. Subsequently, the report looks at the number of electric vehicles that could be supported in the years 2020–2023, depending on the consumption demand and availably of the electricity grid for those future years. The data presented in the article refers to the research study: ‘Electric vehicles in Spain: An overview of charging systems’[1] and ‘Analysis of charging stations for electric vehicles in Spain’ [2].

Coordinating Electric Vehicle Charging Station Installation between the Government and Automakers

Accessibility of Electric Vehicle (EV) charging stations is an important factor for adoption of EV, which is an effective green technology for reducing carbon emissions. Recognizing this, many governments are contemplating ideas for achieving EV adoption targets, such as constructing extra EV charging stations directly or offering subsidies to entice automakers to construct more EV charging stations. To achieve these targets, governments need to coordinate with automakers to ensure that the total number of charging stations is planned optimally. We study this coordination problem by considering the interactions among the government, automakers, and consumers, our equilibrium analysis yields three major results. First, both the government and the automaker should build extra EV charging stations when their construction costs are independent. Simultaneously, the government should offer a per-station subsidy to the automaker only when the adoption target and the construction cost are both high. However, when the construction costs are dependent, the government should delegate the construction to the automaker by offering a per-station subsidy. Second, when the government considers consumer purchase subsidy as an extra lever, we find that the purchase subsidy for consumers is more cost-effective than offering a per-station subsidy to the automaker. Third, the structure of the optimal government policy remains the same regardless of whether the government's goal is to improve EV adoption or consumer welfare. Our results can serve as guidelines for governments when contemplating coordination with automakers for the construction of EV charging stations to improve EV adoption as well as consumer welfare further.

Charge coordination and battery lifecycle analysis of electric vehicles with V2G implementation

With the growing concerns on the energy depletion and the reduction of CO2 emission, electric vehicles (EVs) have gained popularity in transport sector due to clean and reliable energy source. However, the charging of EVs has imposed significant load in the electrical distribution system. The stability of power network is disturbed with uncoordinated charging. This work aims to investigate the optimal EV coordination with vehicle to grid (V2G) technology for the cost-benefit analysis. However, the cost of EV battery replacement due to degradation is of paramount concern for the EV users, which constitute the most important ingredient to ensure active participation in V2G energy exchanges. Therefore, battery degradation cost is formulated for real time analysis taking depth of discharge at each time interval. The firefly algorithm has been used to optimize the system cost. The performance of the proposed system is tested on modified 33 bus distribution system in the presence of renewable energy sources (RES). The impact of system cost and energy losses are analysed for different RES penetration, EV capacities and travelling distances. The simulation results show significant reduction in operating cost when RESs are integrated into distribution network. Moreover, V2G technology is beneficial for EV consumers with high penetration of RES.

Energy efficiency trade-offs in small to large electric vehicles

BackgroundAs electric kick scooters, three-wheelers, and passenger cars enter the streets, efficiency trade-offs across vehicle types gain practical relevance for consumers and policy makers. Here, we compile a comprehensive dataset of 428 electric vehicles, including seven vehicle types and information on certified and real-world energy consumption. Regression analysis is applied to quantify trade-offs between energy consumption and other vehicle attributes.ResultsCertified and real-world energy consumption of electric vehicles increase by 60% and 40%, respectively, with each doubling of vehicle mass, but only by 5% with each doubling of rated motor power. These findings hold roughly also for passenger cars whose energy consumption tends to increase 0.6 ± 0.1 kWh/100 km with each 100 kg of vehicle mass. Battery capacity and vehicle mass are closely related. A 10 kWh increase in battery capacity increases the mass of electric cars by 15 kg, their drive range by 40–50 km, and their energy consumption by 0.7–1.0 kWh/100 km. Mass-produced state-of-the-art electric passenger cars are 2.1 ± 0.8 kWh/100 km more efficient than first-generation vehicles, produced at small scale.ConclusionEfficiency trade-offs in electric vehicles differ from those in conventional cars—the latter showing a strong dependency of fuel consumption on rated engine power. Mass-related efficiency trade-offs in electric vehicles are large and could be tapped by stimulating mode shift from passenger cars to light electric road vehicles. Electric passenger cars still offer potentials for further efficiency improvements. These could be exploited through a dedicated energy label with battery capacity as utility parameter.

Self-Sustainable off grid Electric Vehicle EV Charging Station with Integration of Renewable Sources

Electric Vehicles (EV) are the world’s future transport systems. With the rise in pollutions and its effects on the environment, there has been a large scale movetowards electrical vehicles. But the plug point availability for charging is the serious problem faced by the mostof Electric Vehicle consumers. Therefore, there is a definite need to move from the GRID based/connected charging stations to standalone off-grid stations for charging the Electric Vehicles. The objective of this paper is to arrive at the best configuration or mix of the renewable resources and energy storage systems along with conventional Diesel Generator set which together works in offgrid for Electric Vehicle charging. As aconclusion, by utilizing self-sustainable off-grid power generation technology, the availability of EV charging stations in remote localities at affordable price can be made and mainly it reduces burden on the existing electrical infrastructure.

Pricing for private charging pile sharing considering EV consumers based on non-cooperative game model

The supply of public charging infrastructure is insufficient to meet the charging demand of a large number of electric vehicles (EVs). Private charging pile sharing is an emerging solution to alleviate this imbalance. However, a reasonable price for charging pile sharing has not yet been determined. This study employs a non-cooperative game model to determine a charging pile sharing price considering EV consumers’ charging behaviors. First, a multi-logit model is constructed to measure the probability of EV consumers’ charging behavior choices. Then, a two-matrix game model is established between the private charging pile sharing and public charging mode. Using Beijing as a sample, the sharing rate and price strategies of private and public charging piles are calculated based on the proposed game model. The results show that the optimal sharing rate is 20.01% private charging pile sharing with 1.14 yuan/kWh and 79.99% public charging with 1.7946 yuan/kWh. Sensitivity analysis shows that economics is the most sensitive factor affecting the charging price of private charging pile sharing. Finally, policy recommendations are outlined to improve the private charging pile sharing rate and service efficiency, to broaden the charging options for EV consumers, reduce the construction of public charging piles, and save the government subsidy.

Forecasting the future scale of vehicle to grid technology for electric vehicles and its economic value as future electric energy source: The case of South Korea

With the world seeking ways to cope with climate change, the interest in and demand for electric vehicles are increasing as part of the efforts to resolve the issue of fine dust, especially in South Korea. The Korean government has consistently announced plans to promote electric vehicles as a means of transportation by providing benefits such as subsidies for electric vehicle purchase and expansion of charging infrastructure. Meanwhile, as electric vehicles continue to grow in number, the energy industry has become attentive to its role as a resource for power generation through vehicle to grid technology. This study analyzes electric vehicle consumer preferences using the discrete choice experiment (DCE) and found that there exists a clear nested structure in Korean consumers’ choice of vehicle. The study also estimates the amount of vehicle to grid electricity supply in the power market and calculates not only national but also individual economic benefit of electric vehicle owners participating in vehicle to grid services based on the estimated amount of electricity supplied. The results of scenario analysis indicate that the estimated electric vehicle supply in Korea will be about 560,000 units cumulatively and that the vehicle to grid electric vehicle power supply scale will reach 1.81 GW by 2030. The estimation shows that the economic benefit of vehicle to grid at the national power market level is 50.9 billion KRW per year, while the economic benefit at an individual level (per vehicle) is 104,151 KRW.

A Secure and Anonymous Communication Scheme for Charging Information in Vehicle-to-Grid

With electric vehicle’s charging information, the utilities can increase the efficiency and reliability of Vehicle-to-Grid (V2G) while the electric vehicle consumers can better manage their energy consumption and costs. However, since charging information are commonly transmitted via public network or wireless link, their communications lack trusted third party for identity authentication and key distribution, and are constantly exposed to traffic analysis attacks. In this article, we propose a secure and anonymous communication scheme for delivering charging information in V2G. Different from previous works, the scheme in this article creatively incorporates identity authentication into key distribution without trusted third party, which improves the security of communication in public networks. Moreover, the proposed scheme is more resistant to traffic analysis since it preserves the anonymity of charging information by splitting and forwarding them pseudo-randomly. As the performance analysis reveals, our scheme is able to provide better anonymity without the support of trusted third party, and what’s more, it can achieve high cryptography capability as traditional communication schemes do. Therefore, it is more practical in real-world V2G.

Energy consumption of electric vehicles: models’ estimation using big data (FCD)

The paper presents a framework to estimate energy consumption of Electric Vehicles (EVs) by combining: (a) the use of models derived from traffic flow theory and from mechanics of locomotion and (b) the great amount of Floating Cara Data (FCD) from available Information and Communications Technology (ICT) devices. Existing energy consumption models may be classified into aggregate vs. disaggregate, according to the level of aggregation of variables related to driver, vehicle, and infrastructure. The proposed models have a hybrid nature: the aggregate component allows to estimating the values of vehicular speed and acceleration on a road link; the disaggregate one allows to estimating the discrete variability of EVs’ energy consumption inside a spatial-temporal domain. The energy consumption models are estimated using traffic data extracted from FCD.The proposed framework is structured into four steps: FCD processing, estimation of vehicular speeds and accelerations, estimation of resistance/energy consumption. The framework is applied in a pilot study area, composed by the backward (sub-)urban area of the port of “Porto delle Grazie” of Roccella Jonica (South of Italy). The preliminary results show that the methodology allows relative inexpensive and accurate calculation of EVs’ energy consumption and that it can be integrated into Intelligent Transportation System (ITS) applications.

Predicting Electric Vehicle Consumption: A Hybrid Physical-Empirical Model

Electric vehicles are becoming more important in our society. Using them in a fleet to minimize energy cost is, therefore, a compelling opportunity for taxi companies. It is crucial to develop accurate models that estimate energy consumption for traveling from one point to another. Consumption can be estimated using a physical model, but such a model fails to fit real-world data, especially in taxi-driving conditions. We compare different approaches to learn from historical data in order to correct/improve the physical model. Similar techniques can be used to estimate consumption for a new vehicle model, which can be useful for companies that want to add a new vehicle model for which they do not have historical data.

Symulacyjna analiza energochłonności pojazdów elektrycznych w testach badawczych

The assessment of energy flow through electric vehicle systems makes estimating their energy consumption possible. The article presents analyzes of the energy consumption of electric vehicles in selected driving tests (NEDC, WLTC and in real traffic conditions – RDC test) in relation to the vehicles different curb weight. The use of electric motors was also analyzed, providing their operating ranges, data of the energy flow in batteries and the change in their charge level. Simulation tests and analyzes were carried out using the AVL Cruise software. It was found that despite similar vehicle energy consumption values in NEDC and RDC testing, there are significant differences in energy flow in vehicle subsystems. The changes in the battery charge level per 100 km of test drive are similar in both the WLTC and RDC tests (6% difference); for the NEDC test, this difference is the greatest at 25% (compared to the previous tests). The energy consumption of electric vehicles depends significantly on the test itself; the values obtained in the tests are in the ranges of 10.1–13.5 kWh/100 km (NEDC test); 13–15 kWh/100 km (WLTC test) and 12.5–16.2 kWh/100 km in the RDC test. The energy consumption values in the NEDC and WLTC tests, compared to the RDC test, are approximately 20% and 10% lower, respectively. Increasing the vehicle mass increases the energy consumption (increasing the vehicle mass by 100 kg was found to increase the energy consumption by 0.34 kWh/100 km).

Operational Aspects of Electric Vehicles from Car-Sharing Systems

The article was dedicated to the topic of energy consumption of driving cars equipped with an electric motor. Due to the emerging demands for the excessive use of energy by vehicles (including car-sharing system vehicles), the authors carried out research to determine factors that affect the energy consumption. Due to the occurrence of a research gap related to the lack of reliable scientific information regarding real electricity consumption by vehicles used in car-sharing systems, the authors attempted to determine these values based on the proposed research experiment. The purpose of the research was to identify factors that increase energy consumption while driving in the case of car-sharing systems and developing recommendations for users of car-sharing systems and system operators in relation to energy consumption. Based on data received from car-sharing system operators and to their demands that users move cars uneconomically and use too much energy, the authors performed a scientific experiment based on Hartley’s plan. The authors made journeys with electric cars from car-sharing (measurements) in order to compare real consumption with data obtained from operators. As a result, the authors developed a list of factors that negatively affect the energy consumption of electric vehicles from car-sharing systems. As conclusion, a number of recommendations were developed for car-sharing system operators on how to manage their systems to reduce excessive energy consumption in electric vehicles.

Impact of the Velocity Profile on Energy Consumption of Electric Vehicles

The driving range of electric vehicles is an important issue. The velocity profile can have an important impact on their energy consumption and thus their driving range. In this paper, a commercial electric vehicle is studied. The model of its traction subsystems is developed using a forward approach. A specific driving cycle generator is then coupled with this model. A feedback from the model to the generator allows for recalculation of the velocity reference in case of a limitation due to the electrical drives, for a more accurate estimation of energy consumption. Different trips are then studied with this method. The maximal velocity has a strong impact on the energy consumption while the maximal acceleration has a low impact.